---
language: 
- grc
tags:
- token-classification
- pos-tagging
- ancient-greek
- xlm-roberta
- universal-dependencies
datasets:
- universal_dependencies
widget:
- text: "Μῆνιν ἄειδε θεά Πηληϊάδεω Ἀχιλῆος"
metrics:
- accuracy
- f1
---

# Ancient Greek POS Tagger (XLM-RoBERTa)

This model is a fine-tuned version of [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) for Part-of-Speech (POS) tagging on Ancient Greek. It was trained on the Universal Dependencies [Ancient Greek Perseus treebank](https://github.com/UniversalDependencies/UD_Ancient_Greek-Perseus).

It predicts Universal POS (UPOS) tags (17-label tagset) and achieves an overall test accuracy of **91.38%**.

## Model Details

- **Base Model:** `xlm-roberta-base`
- **Language:** Ancient Greek (`grc`)
- **Task:** Token Classification (Part-of-Speech Tagging)
- **Label Set:** 17 Universal Dependencies POS tags (ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X)

### How to Use

```python
from transformers import pipeline

# Load the POS tagging pipeline
nlp = pipeline(
    "token-classification", 
    model="your-username/your-model-name", 
    aggregation_strategy="first"
)

# Example: Opening line of the Iliad
text = "Μῆνιν ἄειδε θεά Πηληϊάδεω Ἀχιλῆος"
predictions = nlp(text)

for word in predictions:
    print(f"{word['word']:<15} -> {word['entity_group']}")
```

*Expected output:*
```text
Μῆνιν           -> NOUN
ἄειδε           -> VERB
θεά             -> NOUN
Πηληϊάδεω       -> NOUN
Ἀχιλῆος         -> NOUN
```

## Training Data

The model was fine-tuned on the `UD_Ancient_Greek-Perseus` dataset.

## Training Procedure

### Hyperparameters

- **Epochs:** 5
- **Effective Batch Size:** 16 (Batch Size 2 × Gradient Accumulation 8)
- **Learning Rate:** 2e-05
- **Max Sequence Length:** 128
- **Optimizer:** AdamW

### Evaluation Results

- **Overall Accuracy:** 91.38%
- **Weighted Average F1:** 92.18%
- **Macro Average F1:** 73.68%

#### Per-Tag Metrics

| Tag | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| **ADJ** | 0.7742 | 0.7617 | 0.7679 | 1796 |
| **ADP** | 0.9812 | 0.9881 | 0.9846 | 1423 |
| **ADV** | 0.9341 | 0.7602 | 0.8382 | 2481 |
| **AUX** | 0.9247 | 0.9214 | 0.9231 | 280 |
| **CCONJ** | 0.7611 | 0.8728 | 0.8131 | 668 |
| **DET** | 0.9979 | 0.9882 | 0.9930 | 2367 |
| **INTJ** | 0.9688 | 0.8857 | 0.9254 | 35 |
| **NOUN** | 0.9247 | 0.9361 | 0.9304 | 4489 |
| **NUM** | 0.0789 | 0.7500 | 0.1429 | 4 |
| **PART** | 0.0000 | 0.0000 | 0.0000 | 0 |
| **PRON** | 0.8624 | 0.8517 | 0.8570 | 1119 |
| **PUNCT** | 0.9991 | 0.9991 | 0.9991 | 2306 |
| **SCONJ** | 0.9161 | 0.9016 | 0.9088 | 315 |
| **VERB** | 0.9725 | 0.9659 | 0.9692 | 3406 |
| **X** | 0.0000 | 0.0000 | 0.0000 | 1 |

*Note: `PART` and `X` had 0-1 support in the evaluation split, resulting in scores of 0.*

## Framework Versions
- Transformers 
- PyTorch
- Datasets
- Tokenizers